Graphs are ubiquitous representation for structured data. They model naturally occurring relations between objects and, in a sense, generalize sequential data to more complex dependencies. Not surprising, many algorithms originally designed for learning from sequential data are generalized to learning from graph data. Learning vector representations of individual graph nodes, or node embeddings, is such an example where approaches to learning word representations from natural language text have inspired learning from graph data. Node embeddings have become an integral part of the graph learning toolbox, with applications ranging from link prediction [1]}, [2]} to graph compression [3]}.
